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28 :m_wwbb : m_wwbb/F * *Entries : 10000 : Total Size= 40566 bytes File Size = 34410 * *Baskets : 1 : Basket Size= 1500672 bytes Compression= 1.16 * *............................................................................* DataSetInfo : [dataset] : Added class "Signal" : Add Tree sig_tree of type Signal with 10000 events DataSetInfo : [dataset] : Added class "Background" : Add Tree bkg_tree of type Background with 10000 events Factory : Booking method: Likelihood : Factory : Booking method: Fisher : Factory : Booking method: BDT : : Rebuilding Dataset dataset : Building event vectors for type 2 Signal : Dataset[dataset] : create input formulas for tree sig_tree : Building event vectors for type 2 Background : Dataset[dataset] : create input formulas for tree bkg_tree DataSetFactory : [dataset] : Number of events in input trees : : : Number of training and testing events : --------------------------------------------------------------------------- : Signal -- training events : 7000 : Signal -- testing events : 3000 : Signal -- training and testing events: 10000 : Background -- training events : 7000 : Background -- testing events : 3000 : Background -- training and testing events: 10000 : DataSetInfo : Correlation matrix (Signal): : ---------------------------------------------------------------- : m_jj m_jjj m_lv m_jlv m_bb m_wbb m_wwbb : m_jj: +1.000 +0.777 +0.010 +0.107 +0.036 +0.517 +0.532 : m_jjj: +0.777 +1.000 +0.006 +0.083 +0.157 +0.682 +0.669 : m_lv: +0.010 +0.006 +1.000 +0.111 -0.026 +0.011 +0.023 : m_jlv: +0.107 +0.083 +0.111 +1.000 +0.325 +0.550 +0.555 : m_bb: +0.036 +0.157 -0.026 +0.325 +1.000 +0.463 +0.347 : m_wbb: +0.517 +0.682 +0.011 +0.550 +0.463 +1.000 +0.912 : m_wwbb: +0.532 +0.669 +0.023 +0.555 +0.347 +0.912 +1.000 : ---------------------------------------------------------------- DataSetInfo : Correlation matrix (Background): : ---------------------------------------------------------------- : m_jj m_jjj m_lv m_jlv m_bb m_wbb m_wwbb : m_jj: +1.000 +0.804 +0.017 +0.125 +0.007 +0.381 +0.394 : m_jjj: +0.804 +1.000 +0.025 +0.159 +0.153 +0.535 +0.520 : m_lv: +0.017 +0.025 +1.000 +0.114 +0.042 +0.064 +0.069 : m_jlv: +0.125 +0.159 +0.114 +1.000 +0.286 +0.592 +0.542 : m_bb: +0.007 +0.153 +0.042 +0.286 +1.000 +0.623 +0.441 : m_wbb: +0.381 +0.535 +0.064 +0.592 +0.623 +1.000 +0.878 : m_wwbb: +0.394 +0.520 +0.069 +0.542 +0.441 +0.878 +1.000 : ---------------------------------------------------------------- DataSetFactory : [dataset] : : Factory : Booking method: DNN_CPU : : Parsing option string: : ... "!H:V:ErrorStrategy=CROSSENTROPY:VarTransform=G:WeightInitialization=XAVIER:InputLayout=1|1|7:BatchLayout=1|128|7:Layout=DENSE|64|TANH,DENSE|64|TANH,DENSE|64|TANH,DENSE|64|TANH,DENSE|1|LINEAR:TrainingStrategy=LearningRate=1e-3,Momentum=0.9,ConvergenceSteps=10,BatchSize=128,TestRepetitions=1,MaxEpochs=20,WeightDecay=1e-4,Regularization=None,Optimizer=ADAM,ADAM_beta1=0.9,ADAM_beta2=0.999,ADAM_eps=1.E-7,DropConfig=0.0+0.0+0.0+0.:Architecture=CPU" : The following options are set: : - By User: : : - Default: : Boost_num: "0" [Number of times the classifier will be boosted] : Parsing option string: : ... "!H:V:ErrorStrategy=CROSSENTROPY:VarTransform=G:WeightInitialization=XAVIER:InputLayout=1|1|7:BatchLayout=1|128|7:Layout=DENSE|64|TANH,DENSE|64|TANH,DENSE|64|TANH,DENSE|64|TANH,DENSE|1|LINEAR:TrainingStrategy=LearningRate=1e-3,Momentum=0.9,ConvergenceSteps=10,BatchSize=128,TestRepetitions=1,MaxEpochs=20,WeightDecay=1e-4,Regularization=None,Optimizer=ADAM,ADAM_beta1=0.9,ADAM_beta2=0.999,ADAM_eps=1.E-7,DropConfig=0.0+0.0+0.0+0.:Architecture=CPU" : The following options are set: : - By User: : V: "True" [Verbose output (short form of "VerbosityLevel" below - overrides the latter one)] : VarTransform: "G" [List of variable transformations performed before training, e.g., "D_Background,P_Signal,G,N_AllClasses" for: "Decorrelation, PCA-transformation, Gaussianisation, Normalisation, each for the given class of events ('AllClasses' denotes all events of all classes, if no class indication is given, 'All' is assumed)"] : H: "False" [Print method-specific help message] : InputLayout: "1|1|7" [The Layout of the input] : BatchLayout: "1|128|7" [The Layout of the batch] : Layout: "DENSE|64|TANH,DENSE|64|TANH,DENSE|64|TANH,DENSE|64|TANH,DENSE|1|LINEAR" [Layout of the network.] : ErrorStrategy: "CROSSENTROPY" [Loss function: Mean squared error (regression) or cross entropy (binary classification).] : WeightInitialization: "XAVIER" [Weight initialization strategy] : Architecture: "CPU" [Which architecture to perform the training on.] : TrainingStrategy: "LearningRate=1e-3,Momentum=0.9,ConvergenceSteps=10,BatchSize=128,TestRepetitions=1,MaxEpochs=20,WeightDecay=1e-4,Regularization=None,Optimizer=ADAM,ADAM_beta1=0.9,ADAM_beta2=0.999,ADAM_eps=1.E-7,DropConfig=0.0+0.0+0.0+0." [Defines the training strategies.] : - Default: : VerbosityLevel: "Default" [Verbosity level] : CreateMVAPdfs: "False" [Create PDFs for classifier outputs (signal and background)] : IgnoreNegWeightsInTraining: "False" [Events with negative weights are ignored in the training (but are included for testing and performance evaluation)] : RandomSeed: "0" [Random seed used for weight initialization and batch shuffling] : ValidationSize: "20%" [Part of the training data to use for validation. Specify as 0.2 or 20% to use a fifth of the data set as validation set. Specify as 100 to use exactly 100 events. (Default: 20%)] DNN_CPU : [dataset] : Create Transformation "G" with events from all classes. : : Transformation, Variable selection : : Input : variable 'm_jj' <---> Output : variable 'm_jj' : Input : variable 'm_jjj' <---> Output : variable 'm_jjj' : Input : variable 'm_lv' <---> Output : variable 'm_lv' : Input : variable 'm_jlv' <---> Output : variable 'm_jlv' : Input : variable 'm_bb' <---> Output : variable 'm_bb' : Input : variable 'm_wbb' <---> Output : variable 'm_wbb' : Input : variable 'm_wwbb' <---> Output : variable 'm_wwbb' : Will now use the CPU architecture with BLAS and IMT support ! Factory : Train all methods Factory : [dataset] : Create Transformation "I" with events from all classes. : : Transformation, Variable selection : : Input : variable 'm_jj' <---> Output : variable 'm_jj' : Input : variable 'm_jjj' <---> Output : variable 'm_jjj' : Input : variable 'm_lv' <---> Output : variable 'm_lv' : Input : variable 'm_jlv' <---> Output : variable 'm_jlv' : Input : variable 'm_bb' <---> Output : variable 'm_bb' : Input : variable 'm_wbb' <---> Output : variable 'm_wbb' : Input : variable 'm_wwbb' <---> Output : variable 'm_wwbb' TFHandler_Factory : Variable Mean RMS [ Min Max ] : ----------------------------------------------------------- : m_jj: 1.0352 0.65399 [ 0.14661 13.098 ] : m_jjj: 1.0218 0.36964 [ 0.34201 7.3920 ] : m_lv: 1.0497 0.16065 [ 0.26679 3.6823 ] : m_jlv: 1.0126 0.39935 [ 0.38441 6.5831 ] : m_bb: 0.98070 0.53223 [ 0.093482 7.8598 ] : m_wbb: 1.0338 0.35968 [ 0.38503 4.5425 ] : m_wwbb: 0.96049 0.31009 [ 0.43228 4.0728 ] : ----------------------------------------------------------- : Ranking input variables (method unspecific)... IdTransformation : Ranking result (top variable is best ranked) : ------------------------------- : Rank : Variable : Separation : ------------------------------- : 1 : m_bb : 9.114e-02 : 2 : m_wwbb : 4.330e-02 : 3 : m_wbb : 4.241e-02 : 4 : m_jjj : 2.875e-02 : 5 : m_jlv : 1.905e-02 : 6 : m_jj : 3.432e-03 : 7 : m_lv : 2.855e-03 : ------------------------------- Factory : Train method: Likelihood for Classification : : : ================================================================ : H e l p f o r M V A m e t h o d [ Likelihood ] : : : --- Short description: : : The maximum-likelihood classifier models the data with probability : density functions (PDF) reproducing the signal and background : distributions of the input variables. Correlations among the : variables are ignored. : : --- Performance optimisation: : : Required for good performance are decorrelated input variables : (PCA transformation via the option "VarTransform=Decorrelate" : may be tried). Irreducible non-linear correlations may be reduced : by precombining strongly correlated input variables, or by simply : removing one of the variables. : : --- Performance tuning via configuration options: : : High fidelity PDF estimates are mandatory, i.e., sufficient training : statistics is required to populate the tails of the distributions : It would be a surprise if the default Spline or KDE kernel parameters : provide a satisfying fit to the data. The user is advised to properly : tune the events per bin and smooth options in the spline cases : individually per variable. If the KDE kernel is used, the adaptive : Gaussian kernel may lead to artefacts, so please always also try : the non-adaptive one. : : All tuning parameters must be adjusted individually for each input : variable! : : : ================================================================ : : Filling reference histograms : Building PDF out of reference histograms : Elapsed time for training with 14000 events: 0.0742 sec Likelihood : [dataset] : Evaluation of Likelihood on training sample (14000 events) Likelihood : [dataset] : Evaluation of Likelihood on training sample (14000 events) : Elapsed time for evaluation of 14000 events: 0.0108 sec : Elapsed time for evaluation of 14000 events: 0.011 sec : Creating xml weight file: dataset/weights/TMVA_Higgs_Classification_Likelihood.weights.xml : Creating standalone class: dataset/weights/TMVA_Higgs_Classification_Likelihood.class.C : Higgs_ClassificationOutput.root:/dataset/Method_Likelihood/Likelihood Factory : Training finished : Factory : Train method: Fisher for Classification : : : ================================================================ : H e l p f o r M V A m e t h o d [ Fisher ] : : : --- Short description: : : Fisher discriminants select events by distinguishing the mean : values of the signal and background distributions in a trans- : formed variable space where linear correlations are removed. : : (More precisely: the "linear discriminator" determines : an axis in the (correlated) hyperspace of the input : variables such that, when projecting the output classes : (signal and background) upon this axis, they are pushed : as far as possible away from each other, while events : of a same class are confined in a close vicinity. The : linearity property of this classifier is reflected in the : metric with which "far apart" and "close vicinity" are : determined: the covariance matrix of the discriminating : variable space.) : : --- Performance optimisation: : : Optimal performance for Fisher discriminants is obtained for : linearly correlated Gaussian-distributed variables. Any deviation : from this ideal reduces the achievable separation power. In : particular, no discrimination at all is achieved for a variable : that has the same sample mean for signal and background, even if : the shapes of the distributions are very different. Thus, Fisher : discriminants often benefit from suitable transformations of the : input variables. For example, if a variable x in [-1,1] has a : a parabolic signal distributions, and a uniform background : distributions, their mean value is zero in both cases, leading : to no separation. The simple transformation x -> |x| renders this : variable powerful for the use in a Fisher discriminant. : : --- Performance tuning via configuration options: : : : : : ================================================================ : Fisher : Results for Fisher coefficients: : ----------------------- : Variable: Coefficient: : ----------------------- : m_jj: -0.051 : m_jjj: +0.187 : m_lv: +0.037 : m_jlv: +0.065 : m_bb: -0.207 : m_wbb: +0.532 : m_wwbb: -0.743 : (offset): +0.125 : ----------------------- : Elapsed time for training with 14000 events: 0.00513 sec Fisher : [dataset] : Evaluation of Fisher on training sample (14000 events) Fisher : [dataset] : Evaluation of Fisher on training sample (14000 events) : Elapsed time for evaluation of 14000 events: 0.000752 sec : Elapsed time for evaluation of 14000 events: 0.000911 sec : Separation from histogram (PDF): 0.085 (0.000) : Dataset[dataset] : Evaluation of Fisher on training sample : Creating xml weight file: dataset/weights/TMVA_Higgs_Classification_Fisher.weights.xml : Creating standalone class: dataset/weights/TMVA_Higgs_Classification_Fisher.class.C Factory : Training finished : Factory : Train method: BDT for Classification : BDT : #events: (reweighted) sig: 7000 bkg: 7000 : #events: (unweighted) sig: 7000 bkg: 7000 : Training 200 Decision Trees ... patience please : Elapsed time for training with 14000 events: 0.386 sec BDT : [dataset] : Evaluation of BDT on training sample (14000 events) BDT : [dataset] : Evaluation of BDT on training sample (14000 events) : Elapsed time for evaluation of 14000 events: 0.0484 sec : Elapsed time for evaluation of 14000 events: 0.0486 sec : Creating xml weight file: dataset/weights/TMVA_Higgs_Classification_BDT.weights.xml : Creating standalone class: dataset/weights/TMVA_Higgs_Classification_BDT.class.C : Higgs_ClassificationOutput.root:/dataset/Method_BDT/BDT Factory : Training finished : Factory : Train method: DNN_CPU for Classification : : Preparing the Gaussian transformation... TFHandler_DNN_CPU : Variable Mean RMS [ Min Max ] : ----------------------------------------------------------- : m_jj: 0.0042139 0.99787 [ -3.2801 5.7307 ] : m_jjj: 0.0043508 0.99784 [ -3.2805 5.7307 ] : m_lv: 0.0051672 1.0008 [ -3.2813 5.7307 ] : m_jlv: 0.0044388 0.99830 [ -3.2803 5.7307 ] : m_bb: 0.0041864 0.99765 [ -3.2793 5.7307 ] : m_wbb: 0.0046426 0.99950 [ -3.2802 5.7307 ] : m_wwbb: 0.0044594 0.99873 [ -3.2802 5.7307 ] : ----------------------------------------------------------- : Start of deep neural network training on CPU using MT, nthreads = 1 : TFHandler_DNN_CPU : Variable Mean RMS [ Min Max ] : ----------------------------------------------------------- : m_jj: 0.0042139 0.99787 [ -3.2801 5.7307 ] : m_jjj: 0.0043508 0.99784 [ -3.2805 5.7307 ] : m_lv: 0.0051672 1.0008 [ -3.2813 5.7307 ] : m_jlv: 0.0044388 0.99830 [ -3.2803 5.7307 ] : m_bb: 0.0041864 0.99765 [ -3.2793 5.7307 ] : m_wbb: 0.0046426 0.99950 [ -3.2802 5.7307 ] : m_wwbb: 0.0044594 0.99873 [ -3.2802 5.7307 ] : ----------------------------------------------------------- : ***** Deep Learning Network ***** DEEP NEURAL NETWORK: Depth = 5 Input = ( 1, 1, 7 ) Batch size = 128 Loss function = C Layer 0 DENSE Layer: ( Input = 7 , Width = 64 ) Output = ( 1 , 128 , 64 ) Activation Function = Tanh Layer 1 DENSE Layer: ( Input = 64 , Width = 64 ) Output = ( 1 , 128 , 64 ) Activation Function = Tanh Layer 2 DENSE Layer: ( Input = 64 , Width = 64 ) Output = ( 1 , 128 , 64 ) Activation Function = Tanh Layer 3 DENSE Layer: ( Input = 64 , Width = 64 ) Output = ( 1 , 128 , 64 ) Activation Function = Tanh Layer 4 DENSE Layer: ( Input = 64 , Width = 1 ) Output = ( 1 , 128 , 1 ) Activation Function = Identity : Using 11200 events for training and 2800 for testing : Compute initial loss on the validation data : Training phase 1 of 1: Optimizer ADAM (beta1=0.9,beta2=0.999,eps=1e-07) Learning rate = 0.001 regularization 0 minimum error = 0.932334 : -------------------------------------------------------------- : Epoch | Train Err. Val. Err. t(s)/epoch t(s)/Loss nEvents/s Conv. Steps : -------------------------------------------------------------- : Start epoch iteration ... : 1 Minimum Test error found - save the configuration : 1 | 0.662622 0.631763 0.275551 0.020977 43743.7 0 : 2 Minimum Test error found - save the configuration : 2 | 0.608821 0.587526 0.273228 0.0209747 44146.1 0 : 3 Minimum Test error found - save the configuration : 3 | 0.583382 0.58371 0.273358 0.0211011 44145.4 0 : 4 Minimum Test error found - save the configuration : 4 | 0.576627 0.581218 0.274098 0.021042 44006.1 0 : 5 | 0.572304 0.582298 0.277495 0.0209633 43409.9 1 : 6 | 0.568452 0.583847 0.274247 0.0209931 43971.6 2 : 7 | 0.567769 0.581321 0.275289 0.0211868 43824.8 3 : 8 | 0.564124 0.583837 0.27712 0.0211406 43503.4 4 : 9 Minimum Test error found - save the configuration : 9 | 0.562815 0.579549 0.278057 0.0213865 43386.4 0 : 10 Minimum Test error found - save the configuration : 10 | 0.560947 0.576446 0.27726 0.0212959 43506.2 0 : 11 Minimum Test error found - save the configuration : 11 | 0.555196 0.575898 0.276732 0.0215824 43645 0 : 12 | 0.556398 0.581201 0.276957 0.0213826 43572.4 1 : 13 Minimum Test error found - save the configuration : 13 | 0.55574 0.573479 0.277373 0.0212919 43486.3 0 : 14 | 0.551488 0.575989 0.275998 0.021238 43711.8 1 : 15 | 0.551866 0.577309 0.276781 0.0212048 43572.2 2 : 16 | 0.552728 0.575905 0.277738 0.0214875 43457.4 3 : 17 | 0.549064 0.577101 0.277631 0.02136 43454 4 : 18 | 0.54777 0.580918 0.27736 0.0213508 43498.5 5 : 19 | 0.5438 0.578356 0.277069 0.0213518 43548.2 6 : 20 | 0.543572 0.575389 0.27687 0.0213291 43578.2 7 : : Elapsed time for training with 14000 events: 5.58 sec DNN_CPU : [dataset] : Evaluation of DNN_CPU on training sample (14000 events) : Evaluate deep neural network on CPU using batches with size = 128 : DNN_CPU : [dataset] : Evaluation of DNN_CPU on training sample (14000 events) : Elapsed time for evaluation of 14000 events: 0.113 sec : Elapsed time for evaluation of 14000 events: 0.114 sec : Creating xml weight file: dataset/weights/TMVA_Higgs_Classification_DNN_CPU.weights.xml : Creating standalone class: dataset/weights/TMVA_Higgs_Classification_DNN_CPU.class.C Factory : Training finished : : Ranking input variables (method specific)... Likelihood : Ranking result (top variable is best ranked) : ------------------------------------- : Rank : Variable : Delta Separation : ------------------------------------- : 1 : m_bb : 4.649e-02 : 2 : m_wbb : 3.677e-02 : 3 : m_wwbb : 3.283e-02 : 4 : m_lv : -7.659e-04 : 5 : m_jj : -3.645e-03 : 6 : m_jjj : -3.674e-03 : 7 : m_jlv : -1.422e-02 : ------------------------------------- Fisher : Ranking result (top variable is best ranked) : --------------------------------- : Rank : Variable : Discr. power : --------------------------------- : 1 : m_bb : 1.180e-02 : 2 : m_wwbb : 7.816e-03 : 3 : m_wbb : 2.085e-03 : 4 : m_jlv : 5.619e-04 : 5 : m_jjj : 2.327e-04 : 6 : m_lv : 3.319e-05 : 7 : m_jj : 1.479e-05 : --------------------------------- BDT : Ranking result (top variable is best ranked) : ---------------------------------------- : Rank : Variable : Variable Importance : ---------------------------------------- : 1 : m_bb : 2.045e-01 : 2 : m_wwbb : 1.687e-01 : 3 : m_jlv : 1.638e-01 : 4 : m_jjj : 1.413e-01 : 5 : m_wbb : 1.356e-01 : 6 : m_jj : 1.080e-01 : 7 : m_lv : 7.813e-02 : ---------------------------------------- : No variable ranking supplied by classifier: DNN_CPU TH1.Print Name = TrainingHistory_DNN_CPU_trainingError, Entries= 0, Total sum= 11.3355 TH1.Print Name = TrainingHistory_DNN_CPU_valError, Entries= 0, Total sum= 11.6431 Factory : === Destroy and recreate all methods via weight files for testing === : : Reading weight file: dataset/weights/TMVA_Higgs_Classification_Likelihood.weights.xml : Reading weight file: dataset/weights/TMVA_Higgs_Classification_Fisher.weights.xml : Reading weight file: dataset/weights/TMVA_Higgs_Classification_BDT.weights.xml : Reading weight file: dataset/weights/TMVA_Higgs_Classification_DNN_CPU.weights.xml Factory : Test all methods Factory : Test method: Likelihood for Classification performance : Likelihood : [dataset] : Evaluation of Likelihood on testing sample (6000 events) Likelihood : [dataset] : Evaluation of Likelihood on testing sample (6000 events) : Elapsed time for evaluation of 6000 events: 0.00487 sec : Elapsed time for evaluation of 6000 events: 0.00499 sec Factory : Test method: Fisher for Classification performance : Fisher : [dataset] : Evaluation of Fisher on testing sample (6000 events) Fisher : [dataset] : Evaluation of Fisher on testing sample (6000 events) : Elapsed time for evaluation of 6000 events: 0.000477 sec : Elapsed time for evaluation of 6000 events: 0.000575 sec : Dataset[dataset] : Evaluation of Fisher on testing sample Factory : Test method: BDT for Classification performance : BDT : [dataset] : Evaluation of BDT on testing sample (6000 events) BDT : [dataset] : Evaluation of BDT on testing sample (6000 events) : Elapsed time for evaluation of 6000 events: 0.0185 sec : Elapsed time for evaluation of 6000 events: 0.0186 sec Factory : Test method: DNN_CPU for Classification performance : DNN_CPU : [dataset] : Evaluation of DNN_CPU on testing sample (6000 events) : Evaluate deep neural network on CPU using batches with size = 1000 : TFHandler_DNN_CPU : Variable Mean RMS [ Min Max ] : ----------------------------------------------------------- : m_jj: 0.029995 0.98065 [ -3.1064 5.7307 ] : m_jjj: 0.030151 0.98464 [ -2.9982 5.7307 ] : m_lv: 0.011988 1.0066 [ -3.2274 5.7307 ] : m_jlv: 0.0049774 1.0015 [ -3.0644 5.7307 ] : m_bb: -0.036143 1.0111 [ -5.7307 5.7307 ] : m_wbb: -0.0056377 1.0239 [ -3.0260 5.7307 ] : m_wwbb: 0.0023364 1.0091 [ -3.1905 5.7307 ] : ----------------------------------------------------------- DNN_CPU : [dataset] : Evaluation of DNN_CPU on testing sample (6000 events) : Elapsed time for evaluation of 6000 events: 0.047 sec : Elapsed time for evaluation of 6000 events: 0.0578 sec Factory : Evaluate all methods Factory : Evaluate classifier: Likelihood : Likelihood : [dataset] : Loop over test events and fill histograms with classifier response... : : maximum iterations (100) reached before convergence : maximum iterations (100) reached before convergence TFHandler_Likelihood : Variable Mean RMS [ Min Max ] : ----------------------------------------------------------- : m_jj: 1.0368 0.66752 [ 0.16310 16.132 ] : m_jjj: 1.0272 0.38070 [ 0.41899 8.9401 ] : m_lv: 1.0522 0.17017 [ 0.29757 3.2605 ] : m_jlv: 1.0135 0.40315 [ 0.41660 5.8195 ] : m_bb: 0.96616 0.53867 [ 0.080986 8.2551 ] : m_wbb: 1.0344 0.37776 [ 0.42068 6.4013 ] : m_wwbb: 0.96122 0.31782 [ 0.44118 4.5350 ] : ----------------------------------------------------------- Factory : Evaluate classifier: Fisher : Fisher : [dataset] : Loop over test events and fill histograms with classifier response... : : Also filling probability and rarity histograms (on request)... TFHandler_Fisher : Variable Mean RMS [ Min Max ] : ----------------------------------------------------------- : m_jj: 1.0368 0.66752 [ 0.16310 16.132 ] : m_jjj: 1.0272 0.38070 [ 0.41899 8.9401 ] : m_lv: 1.0522 0.17017 [ 0.29757 3.2605 ] : m_jlv: 1.0135 0.40315 [ 0.41660 5.8195 ] : m_bb: 0.96616 0.53867 [ 0.080986 8.2551 ] : m_wbb: 1.0344 0.37776 [ 0.42068 6.4013 ] : m_wwbb: 0.96122 0.31782 [ 0.44118 4.5350 ] : ----------------------------------------------------------- Factory : Evaluate classifier: BDT : BDT : [dataset] : Loop over test events and fill histograms with classifier response... : TFHandler_BDT : Variable Mean RMS [ Min Max ] : ----------------------------------------------------------- : m_jj: 1.0368 0.66752 [ 0.16310 16.132 ] : m_jjj: 1.0272 0.38070 [ 0.41899 8.9401 ] : m_lv: 1.0522 0.17017 [ 0.29757 3.2605 ] : m_jlv: 1.0135 0.40315 [ 0.41660 5.8195 ] : m_bb: 0.96616 0.53867 [ 0.080986 8.2551 ] : m_wbb: 1.0344 0.37776 [ 0.42068 6.4013 ] : m_wwbb: 0.96122 0.31782 [ 0.44118 4.5350 ] : ----------------------------------------------------------- Factory : Evaluate classifier: DNN_CPU : DNN_CPU : [dataset] : Loop over test events and fill histograms with classifier response... : : Evaluate deep neural network on CPU using batches with size = 1000 : TFHandler_DNN_CPU : Variable Mean RMS [ Min Max ] : ----------------------------------------------------------- : m_jj: 0.0042139 0.99787 [ -3.2801 5.7307 ] : m_jjj: 0.0043508 0.99784 [ -3.2805 5.7307 ] : m_lv: 0.0051673 1.0008 [ -3.2813 5.7307 ] : m_jlv: 0.0044388 0.99830 [ -3.2803 5.7307 ] : m_bb: 0.0041864 0.99765 [ -3.2793 5.7307 ] : m_wbb: 0.0046426 0.99950 [ -3.2802 5.7307 ] : m_wwbb: 0.0044594 0.99873 [ -3.2802 5.7307 ] : ----------------------------------------------------------- TFHandler_DNN_CPU : Variable Mean RMS [ Min Max ] : ----------------------------------------------------------- : m_jj: 0.029995 0.98065 [ -3.1064 5.7307 ] : m_jjj: 0.030151 0.98464 [ -2.9982 5.7307 ] : m_lv: 0.011988 1.0066 [ -3.2274 5.7307 ] : m_jlv: 0.0049774 1.0015 [ -3.0644 5.7307 ] : m_bb: -0.036143 1.0111 [ -5.7307 5.7307 ] : m_wbb: -0.0056377 1.0239 [ -3.0260 5.7307 ] : m_wwbb: 0.0023364 1.0091 [ -3.1905 5.7307 ] : ----------------------------------------------------------- : : Evaluation results ranked by best signal efficiency and purity (area) : ------------------------------------------------------------------------------------------------------------------- : DataSet MVA : Name: Method: ROC-integ : dataset DNN_CPU : 0.765 : dataset BDT : 0.758 : dataset Likelihood : 0.700 : dataset Fisher : 0.654 : ------------------------------------------------------------------------------------------------------------------- : : Testing efficiency compared to training efficiency (overtraining check) : ------------------------------------------------------------------------------------------------------------------- : DataSet MVA Signal efficiency: from test sample (from training sample) : Name: Method: @B=0.01 @B=0.10 @B=0.30 : ------------------------------------------------------------------------------------------------------------------- : dataset DNN_CPU : 0.120 (0.145) 0.409 (0.434) 0.682 (0.702) : dataset BDT : 0.080 (0.095) 0.394 (0.394) 0.674 (0.685) : dataset Likelihood : 0.066 (0.080) 0.313 (0.334) 0.585 (0.593) : dataset Fisher : 0.017 (0.014) 0.128 (0.141) 0.500 (0.529) : ------------------------------------------------------------------------------------------------------------------- : Dataset:dataset : Created tree 'TestTree' with 6000 events : Dataset:dataset : Created tree 'TrainTree' with 14000 events : Factory : Thank you for using TMVA! : For citation information, please visit: http://tmva.sf.net/citeTMVA.html